Ignite Creation Date:
2024-05-06 @ 8:17 PM
Last Modification Date:
2024-10-26 @ 3:24 PM
Study NCT ID:
NCT06321614
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2024-03-20
First Post:
2024-03-13
Brief Title:
Deep Learning in Classifying Bowel Obstruction Radiographs
Sponsor:
The First Affiliated Hospital of Soochow University
Organization:
The First Affiliated Hospital of Soochow University
Study Overview
Official Title:
Self-supervised Learning for Classifying Bowel Obstruction on Upright Abdominal Radiography
Status:
ACTIVE_NOT_RECRUITING
Status Verified Date:
2024-03
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
Background Accurate labeling of obstruction site on upright abdominal radiograph is a challenging task The lack of ground truth leads to poor performance on supervised learning models To address this issue self-supervised learning SSL is proposed to classify normal small bowel obstruction SBO and large bowel obstruction LBO radiographs using a few confirmed samples
Methods A few number of confirmed and a large number of unlabeled radiographs were categorized based on the ground truth The SSL model was firstly trained on the unlabeled radiographs and then fine-tuned on the confirmed radiographs ResNet50 and VGG16 were used for the embedded base encoders whose weights and parameters were adjusted during training process Furthermore it was tested on an independent dataset compared with supervised learning models and human interpreters Finally the t-SNE and Grad-CAM were used to visualize the models interpretation
Methods A few number of confirmed and a large number of unlabeled radiographs were categorized based on the ground truth The SSL model was firstly trained on the unlabeled radiographs and then fine-tuned on the confirmed radiographs ResNet50 and VGG16 were used for the embedded base encoders whose weights and parameters were adjusted during training process Furthermore it was tested on an independent dataset compared with supervised learning models and human interpreters Finally the t-SNE and Grad-CAM were used to visualize the models interpretation
Detailed Description:
None
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None